From bc9b69a6f07b22e6b26483d390ad593aec349023 Mon Sep 17 00:00:00 2001
From: chong.zhang <chong.zhang@alibaba-inc.com>
Date: 星期二, 23 五月 2023 16:44:00 +0800
Subject: [PATCH] add speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch
---
/dev/null | 187 ----------------------------------------------
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/demo.py | 10 ++
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/README.md | 1
3 files changed, 11 insertions(+), 187 deletions(-)
diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/README.md b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/README.md
deleted file mode 100644
index 3e5cc02..0000000
--- a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/README.md
+++ /dev/null
@@ -1,187 +0,0 @@
-# Speech Recognition
-
-> **Note**:
-> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take the typic models as examples to demonstrate the usage.
-
-## Inference
-
-### Quick start
-#### [Paraformer Model](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/summary)
-```python
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-
-inference_pipeline = pipeline(
- task=Tasks.auto_speech_recognition,
- model='damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch',
-)
-
-rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_tr.wav')
-print(rec_result)
-```
-#### [UniASR Turkish Model](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/summary)
-There are three decoding mode for UniASR model(`fast`銆乣normal`銆乣offline`), for more model details, please refer to [docs](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary)
-```python
-decoding_model = "fast" # "fast"銆�"normal"銆�"offline"
-inference_pipeline = pipeline(
- task=Tasks.auto_speech_recognition,
- model='damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch',
- param_dict={"decoding_model": decoding_model})
-
-rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_tr.wav')
-print(rec_result)
-```
-The decoding mode of `fast` and `normal` is fake streaming, which could be used for evaluating of recognition accuracy.
-Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/151)
-
-
-### API-reference
-#### Define pipeline
-- `task`: `Tasks.auto_speech_recognition`
-- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
-- `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
-- `ncpu`: `1` (Default), sets the number of threads used for intraop parallelism on CPU
-- `output_dir`: `None` (Default), the output path of results if set
-- `batch_size`: `1` (Default), batch size when decoding
-#### Infer pipeline
-- `audio_in`: the input to decode, which could be:
- - wav_path, `e.g.`: asr_example.wav,
- - pcm_path, `e.g.`: asr_example.pcm,
- - audio bytes stream, `e.g.`: bytes data from a microphone
- - audio sample point锛宍e.g.`: `audio, rate = soundfile.read("asr_example_tr.wav")`, the dtype is numpy.ndarray or torch.Tensor
- - wav.scp, kaldi style wav list (`wav_id \t wav_path`), `e.g.`:
- ```text
- asr_example1 ./audios/asr_example1.wav
- asr_example2 ./audios/asr_example2.wav
- ```
- In this case of `wav.scp` input, `output_dir` must be set to save the output results
-- `audio_fs`: audio sampling rate, only set when audio_in is pcm audio
-- `output_dir`: None (Default), the output path of results if set
-
-### Inference with multi-thread CPUs or multi GPUs
-FunASR also offer recipes [egs_modelscope/asr/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs.
-
-#### Settings of `infer.sh`
-- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
-- `data_dir`: the dataset dir needs to include `wav.scp`. If `${data_dir}/text` is also exists, CER will be computed
-- `output_dir`: output dir of the recognition results
-- `batch_size`: `64` (Default), batch size of inference on gpu
-- `gpu_inference`: `true` (Default), whether to perform gpu decoding, set false for CPU inference
-- `gpuid_list`: `0,1` (Default), which gpu_ids are used to infer
-- `njob`: only used for CPU inference (`gpu_inference`=`false`), `64` (Default), the number of jobs for CPU decoding
-- `checkpoint_dir`: only used for infer finetuned models, the path dir of finetuned models
-- `checkpoint_name`: only used for infer finetuned models, `valid.cer_ctc.ave.pb` (Default), which checkpoint is used to infer
-- `decoding_mode`: `normal` (Default), decoding mode for UniASR model(fast銆乶ormal銆乷ffline)
-- `hotword_txt`: `None` (Default), hotword file for contextual paraformer model(the hotword file name ends with .txt")
-
-
-#### Decode with multi-thread CPUs:
-```shell
- bash infer.sh \
- --model "damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch" \
- --data_dir "./data/test" \
- --output_dir "./results" \
- --gpu_inference false \
- --njob 64
-```
-
-#### Results
-
-The decoding results can be found in `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.
-
-If you decode the SpeechIO test sets, you can use textnorm with `stage`=3, and `DETAILS.txt`, `RESULTS.txt` record the results and CER after text normalization.
-
-
-## Finetune with pipeline
-
-### Quick start
-[finetune.py](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/finetune.py)
-```python
-import os
-from modelscope.metainfo import Trainers
-from modelscope.trainers import build_trainer
-from modelscope.msdatasets.audio.asr_dataset import ASRDataset
-
-def modelscope_finetune(params):
- if not os.path.exists(params.output_dir):
- os.makedirs(params.output_dir, exist_ok=True)
- # dataset split ["train", "validation"]
- ds_dict = ASRDataset.load(params.data_path, namespace='speech_asr')
- kwargs = dict(
- model=params.model,
- data_dir=ds_dict,
- dataset_type=params.dataset_type,
- work_dir=params.output_dir,
- batch_bins=params.batch_bins,
- max_epoch=params.max_epoch,
- lr=params.lr)
- trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
- trainer.train()
-
-
-if __name__ == '__main__':
- from funasr.utils.modelscope_param import modelscope_args
- params = modelscope_args(model="damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch")
- params.output_dir = "./checkpoint" # 妯″瀷淇濆瓨璺緞
- params.data_path = "speech_asr_aishell1_trainsets" # 鏁版嵁璺緞锛屽彲浠ヤ负modelscope涓凡涓婁紶鏁版嵁锛屼篃鍙互鏄湰鍦版暟鎹�
- params.dataset_type = "small" # 灏忔暟鎹噺璁剧疆small锛岃嫢鏁版嵁閲忓ぇ浜�1000灏忔椂锛岃浣跨敤large
- params.batch_bins = 2000 # batch size锛屽鏋渄ataset_type="small"锛宐atch_bins鍗曚綅涓篺bank鐗瑰緛甯ф暟锛屽鏋渄ataset_type="large"锛宐atch_bins鍗曚綅涓烘绉掞紝
- params.max_epoch = 50 # 鏈�澶ц缁冭疆鏁�
- params.lr = 0.00005 # 璁剧疆瀛︿範鐜�
-
- modelscope_finetune(params)
-```
-
-```shell
-python finetune.py &> log.txt &
-```
-
-### Finetune with your data
-
-- Modify finetune training related parameters in [finetune.py](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/finetune.py)
- - `output_dir`: result dir
- - `data_dir`: the dataset dir needs to include files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text`
- - `dataset_type`: for dataset larger than 1000 hours, set as `large`, otherwise set as `small`
- - `batch_bins`: batch size. For dataset_type is `small`, `batch_bins` indicates the feature frames. For dataset_type is `large`, `batch_bins` indicates the duration in ms
- - `max_epoch`: number of training epoch
- - `lr`: learning rate
-
-- Training data formats锛�
-```sh
-cat ./example_data/text
-BAC009S0002W0122 鑰� 瀵� 妤� 甯� 鎴� 浜� 鎶� 鍒� 浣� 鐢� 鏈� 澶� 鐨� 闄� 璐�
-BAC009S0002W0123 涔� 鎴� 涓� 鍦� 鏂� 鏀� 搴� 鐨� 鐪� 涓� 閽�
-english_example_1 hello world
-english_example_2 go swim 鍘� 娓� 娉�
-
-cat ./example_data/wav.scp
-BAC009S0002W0122 /mnt/data/wav/train/S0002/BAC009S0002W0122.wav
-BAC009S0002W0123 /mnt/data/wav/train/S0002/BAC009S0002W0123.wav
-english_example_1 /mnt/data/wav/train/S0002/english_example_1.wav
-english_example_2 /mnt/data/wav/train/S0002/english_example_2.wav
-```
-
-- Then you can run the pipeline to finetune with:
-```shell
-python finetune.py
-```
-If you want finetune with multi-GPUs, you could:
-```shell
-CUDA_VISIBLE_DEVICES=1,2 python -m torch.distributed.launch --nproc_per_node 2 finetune.py > log.txt 2>&1
-```
-## Inference with your finetuned model
-
-- Setting parameters in [egs_modelscope/asr/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) is the same with [docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/egs_modelscope/asr/TEMPLATE#inference-with-multi-thread-cpus-or-multi-gpus), `model` is the model name from modelscope, which you finetuned.
-
-
-- Decode with multi-thread CPUs:
-```shell
- bash infer.sh \
- --model "damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch" \
- --data_dir "./data/test" \
- --output_dir "./results" \
- --gpu_inference false \
- --njob 64 \
- --checkpoint_dir "./checkpoint" \
- --checkpoint_name "valid.cer_ctc.ave.pb"
-```
diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/README.md b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/README.md
new file mode 120000
index 0000000..bb55ab5
--- /dev/null
+++ b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/README.md
@@ -0,0 +1 @@
+../../TEMPLATE/README.md
\ No newline at end of file
diff --git a/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/demo.py b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/demo.py
new file mode 100644
index 0000000..181c305
--- /dev/null
+++ b/egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/demo.py
@@ -0,0 +1,10 @@
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch',
+ model_revision='v1.0.0')
+
+rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_tr.wav')
+print(rec_result)
\ No newline at end of file
--
Gitblit v1.9.1